Walking is like slithering: A unifying, data-driven view of locomotion

Abstract

Legged movement is ubiquitous in nature and of increasing interest for robotics. Most legged animals routinely encounter foot slipping, yet detailed modeling of multiple contacts with slipping exceeds current simulation capacity. Here we present a principle that unifies multilegged walking (including that involving slipping) with slithering and Stokesian (low Reynolds number) swimming. We generated data-driven principally kinematic models of locomotion for walking in low-slip animals (Argentine ant, 4.7% slip ratio of slipping to total motion) and for high-slip robotic systems (BigANT hexapod, slip ratio 12 to 22%; Multipod robots ranging from 6 to 12 legs, slip ratio 40 to 100%). We found that principally kinematic models could explain much of the variability in body velocity and turning rate using body shape and could predict walking behaviors outside the training data. Most remarkably, walking was principally kinematic irrespective of leg number, foot slipping, and turning rate. We find that grounded walking, with or without slipping, is governed by principally kinematic equations of motion, functionally similar to frictional swimming and slithering. Geometric mechanics thus leads to a unified model for swimming, slithering, and walking. Such commonality may shed light on the evolutionary origins of animal locomotion control and offer new approaches for robotic locomotion and motion planning.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 06, 2022
Source ID
10.1073/pnas.2113222119

Entities

People

  • Brian Bittner
  • Dan Zhao
  • Glenna T Clifton
  • Nick Gravish
  • Shai Revzen

Organizations

  • Army Research Office
  • Johns Hopkins University
  • National Science Foundation
  • University of California, San Diego
  • University of Michigan
  • University of Portland

Tags

Readers

  • Mathematics or Statistics
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy